Progress on the demand side management in smart grid and optimization approaches

被引:137
|
作者
Sarker, Eity [1 ]
Halder, Pobitra [2 ]
Seyedmahmoudian, Mehdi [1 ]
Jamei, Elmira [3 ]
Horan, Ben [4 ]
Mekhilef, Saad [5 ]
Stojcevski, Alex [1 ]
机构
[1] Swinburne Univ Technol, Sch Software & Elect Engn, Melbourne, Vic 3122, Australia
[2] RMIT Univ, Sch Engn, Melbourne, Vic, Australia
[3] Victoria Univ, Coll Engn & Sci, Melbourne, Vic, Australia
[4] Deakin Univ, Sch Engn, Geelong, Vic, Australia
[5] Univ Malaya, Dept Elect Engn, Kuala Lumpur, Malaysia
关键词
algorithms; demand side management; load management; optimization; peak loads; smart grid; sustainability; PARTICLE SWARM OPTIMIZATION; RENEWABLE ENERGY-SYSTEMS; ANT COLONY OPTIMIZATION; DIRECT LOAD CONTROL; RESPONSE MANAGEMENT; GENETIC ALGORITHMS; POWER-SYSTEMS; GENERATION; CONSUMPTION; CHALLENGES;
D O I
10.1002/er.5631
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The integration of demand side management (DSM) with smart grid (SG) can facilitate residents' transfer into smart homes and sustainable cities by reducing the carbon emission. This manuscript reviews the recent works related to the application of DSM in SG through discussing the techniques and algorithms and their associated challenges for effective implementation. This paper also critically discusses the operation mode of DSM, the profile of energy production, storage and consumption, and finally the benefit obtained by the DSM implementation. Previous literature suggested that DSM practice reduced peak-to-average ratio, energy cost and carbon emission by approximately 10% to 65%, 5% to 50%, and 14%, respectively. The implementation of DSM in SG deals with a number of challenges such as security and privacy, tariff regulation, energy transmission, distribution, and effective utilization of energy resources. A number of international organizations have taken various measures and solutions to guarantee the security and privacy of the DSM in SG discussed. So far, a number of algorithms have been used as optimization approach to solve the DSM optimization problems; however hybrid algorithms have showed better performance than single algorithms due to their faster convergence speed. At the end, the paper presents the research gaps and future research directions.
引用
收藏
页码:36 / 64
页数:29
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